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Course Outline
Introduction
- Artificial neural networks vs decision tree based algorithms
Overview of XGBoost Features
- Elements of a Gradient Boosting algorithm
- Focus on computational speed and model performance
- XGBoost vs Logistic Regression, Random Forest, and standard Gradient Boosting
The Evolution of Tree-Based Algorithms
- Decision Trees, Bagging, Random Forest, Boosting, Gradient Boosting
- System optimization
- Algorithmic enhancements
Preparing the Environment
- Installing SciPy and scikit-learn
Creating a XGBoost Model
- Downloading a data set
- Solving a common classification problem
- Training the XGBoost model for classification
- Solve a common regression task
Monitoring Performance
- Evaluating and reporting performance
- Early Stopping
Plotting Features by Importance
- Calculating feature importance
- Deciding which input variables to keep or discard
Configuring Gradient Boosting
- Review the learning curves on training and validation datasets
- Adjusting the learning rate
- Adjusting the number of trees
Hyperparameter Tuning
- Improving the performance of an XGBoost model
- Designing a controlled experiment to tune hyperparameters
- Searching combinations of parameters
Creating a Pipeline
- Incorporating an XGBoost model into an end-to-end machine learning pipeline
- Tuning hyperparameters within the pipeline
- Advanced preprocessing techniques
Troubleshooting
Summary and Conclusion
Requirements
- Experience writing machine learning models
Audience
- Data scientists
- Machine learning engineers
Testimonials
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
The trainer very easily explained difficult and advanced topics.
Leszek K
All like it
蒙 李
Communication with lecturers
文欣 张
like it all
lisa xie
I genuinely liked excercises
- L M ERICSSON LIMITED
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
- L M ERICSSON LIMITED
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
- Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
I like that it focuses more on the how-to of the different text summarization methods
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.
Rock Solid Knowledge Ltd
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!
Rock Solid Knowledge Ltd
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
convolution filter
Francesco Ferrara - Inpeco SpA
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
I like that it focuses more on the how-to of the different text summarization methods
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